Search Results for author:
Found 9 papers, 0 papers with code
ChatGen: Automatic Text-to-Image Generation From FreeStyle Chatting
Despite the significant advancements in text-to-image (T2I) generative models, users often face a trial-and-error challenge in practical scenarios.
AgentStore: Scalable Integration of Heterogeneous Agents As Specialized Generalist Computer Assistant
Comprehensive quantitative and qualitative results further demonstrate AgentStore's ability to enhance agent systems in both generalization and specialization, underscoring its potential for developing the specialized generalist computer assistant.
Disentangled Noisy Correspondence Learning
Moreover, although intuitive, directly applying previous cross-modal disentanglement methods suffers from limited noise tolerance and disentanglement efficacy.
Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation
Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice.
Cross-modal retrieval with noisy correspondence
Memorization
+2
Generating Action-conditioned Prompts for Open-vocabulary Video Action Recognition
To realize this, we innovatively blend video models with Large Language Models (LLMs) to devise Action-conditioned Prompts.
Disentangled Representation Learning with Transmitted Information Bottleneck
Encoding only the task-related information from the raw data, \ie, disentangled representation learning, can greatly contribute to the robustness and generalizability of models.
PSDiff: Diffusion Model for Person Search with Iterative and Collaborative Refinement
Dominant Person Search methods aim to localize and recognize query persons in a unified network, which jointly optimizes two sub-tasks, \ie, pedestrian detection and Re-IDentification (ReID).
Disentangled Generation with Information Bottleneck for Few-Shot Learning
To these challenges, we propose a novel Information Bottleneck (IB) based Disentangled Generation Framework for FSL, termed as DisGenIB, that can simultaneously guarantee the discrimination and diversity of generated samples.
